World Happiness Report Data Analysis Project

Ashley Bissell AD450 Final Project Winter 2025

Thesis Statement:

Exploring factors that have the strongest impact on world happiness scores, and how happiness differs between countries and across time.

Exploratory Data Analysis

Summarize the data

I used .info() to see all of the columns and data types

Happiness Dataset Info: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4000 entries, 0 to 3999
Data columns (total 24 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Country                    4000 non-null   object 
 1   Year                       4000 non-null   int64  
 2   Happiness_Score            4000 non-null   float64
 3   GDP_per_Capita             4000 non-null   float64
 4   Social_Support             4000 non-null   float64
 5   Healthy_Life_Expectancy    4000 non-null   float64
 6   Freedom                    4000 non-null   float64
 7   Generosity                 4000 non-null   float64
 8   Corruption_Perception      4000 non-null   float64
 9   Unemployment_Rate          4000 non-null   float64
 10  Education_Index            4000 non-null   float64
 11  Population                 4000 non-null   int64  
 12  Urbanization_Rate          4000 non-null   float64
 13  Life_Satisfaction          4000 non-null   float64
 14  Public_Trust               4000 non-null   float64
 15  Mental_Health_Index        4000 non-null   float64
 16  Income_Inequality          4000 non-null   float64
 17  Public_Health_Expenditure  4000 non-null   float64
 18  Climate_Index              4000 non-null   float64
 19  Work_Life_Balance          4000 non-null   float64
 20  Internet_Access            4000 non-null   float64
 21  Crime_Rate                 4000 non-null   float64
 22  Political_Stability        4000 non-null   float64
 23  Employment_Rate            4000 non-null   float64
dtypes: float64(21), int64(2), object(1)
memory usage: 750.1+ KB

Get basic statistics

I used .describe() to see the ranges of the values and make sure data seems reasonable/real

Summary Statistics: 

	Year	Happiness_Score	GDP_per_Capita	Social_Support	Healthy_Life_Expectancy	Freedom	Generosity	Corruption_Perception	Unemployment_Rate	Education_Index	...	Public_Trust	Mental_Health_Index	Income_Inequality	Public_Health_Expenditure	Climate_Index	Work_Life_Balance	Internet_Access	Crime_Rate	Political_Stability	Employment_Rate
count	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	...	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000	4000.000000
mean	2014.670750	5.455005	30482.009953	0.505860	67.917605	0.502723	0.143960	0.498920	10.966748	0.750385	...	0.502812	69.976853	40.002648	6.009270	65.176380	5.987325	67.586327	45.526322	0.494105	74.021450
std	5.724075	1.427370	17216.122032	0.286202	10.172091	0.285219	0.200088	0.288866	5.210712	0.144819	...	0.289186	17.128536	11.634987	2.291172	19.981357	1.725363	15.769023	20.300069	0.293191	13.906888
min	2005.000000	3.000000	1009.310000	0.000000	50.000000	0.000000	-0.200000	0.000000	2.000000	0.500000	...	0.000000	40.000000	20.010000	2.010000	30.010000	3.000000	40.010000	10.030000	0.000000	50.000000
25%	2010.000000	4.237500	15425.125000	0.260000	59.177500	0.260000	-0.030000	0.240000	6.450000	0.630000	...	0.260000	55.580000	29.865000	4.040000	48.170000	4.460000	53.910000	27.840000	0.230000	61.867500
50%	2015.000000	5.430000	29991.255000	0.510000	68.015000	0.500000	0.140000	0.500000	10.995000	0.750000	...	0.500000	69.650000	40.015000	6.070000	64.755000	6.020000	68.015000	45.760000	0.490000	74.475000
75%	2020.000000	6.662500	45763.085000	0.750000	76.690000	0.750000	0.310000	0.742500	15.450000	0.880000	...	0.760000	84.582500	50.187500	8.010000	82.652500	7.490000	81.332500	63.197500	0.760000	85.912500
max	2024.000000	8.000000	59980.720000	1.000000	85.000000	1.000000	0.500000	1.000000	19.990000	1.000000	...	1.000000	100.000000	59.970000	10.000000	99.990000	9.000000	94.990000	79.990000	1.000000	98.000000

8 rows × 23 columns

Get value counts of a categorical column

I used .value_counts() to see how the data was spread across countries

Value Counts: 

Country
USA             429
France          415
Germany         413
Brazil          404
Australia       400
India           399
UK              395
Canada          386
South Africa    385
China           374
Name: count, dtype: int64

Get histograms of numeric columns

I used .hist() to see how each column's data is spread

Look for relationships in the data

I used scatter_matrix to see if there were any obvious connections between happiness_score and any other variables, or between subsets of variables (all on one scatter_matrix was too difficult to see due to large number of variables)

Data Cleaning and Transformation

Filling NaN values

I used .isna() to find missing values (there were none)

There is no missing data.

2. Correct data dtype issues

I used .to_datetime to convert year in int64 to year in datetime (later I have to reverse this)

New dtype for Year column:  datetime64[ns]

Data Joining

Merging two or more dataframes on a column

I created a dataframe of continents, and merged on country

	Country	Year	Happiness_Score	GDP_per_Capita	Social_Support	Healthy_Life_Expectancy	Freedom	Generosity	Corruption_Perception	Unemployment_Rate	Education_Index	Population	Urbanization_Rate	Life_Satisfaction	Public_Trust	Mental_Health_Index	Income_Inequality	Public_Health_Expenditure	Climate_Index	Work_Life_Balance	Internet_Access	Crime_Rate	Political_Stability	Employment_Rate	Continent
0	China	2022-01-01	4.39	44984.68	0.53	71.11	0.41	-0.05	0.83	14.98	0.52	1311940760	78.71	8.88	0.34	76.44	46.06	8.92	62.75	8.59	74.40	70.30	0.29	61.38	Asia
1	UK	2015-01-01	5.49	30814.59	0.93	63.14	0.89	0.04	0.84	19.46	0.83	1194240877	50.87	5.03	0.72	53.38	46.43	4.43	53.11	8.76	91.74	73.32	0.76	80.18	Europe
2	Brazil	2009-01-01	4.65	39214.84	0.03	62.36	0.01	0.16	0.59	16.68	0.95	731100898	48.75	5.22	0.23	82.40	31.03	3.78	33.30	6.06	71.80	28.99	0.94	72.65	South America
3	France	2019-01-01	5.20	30655.75	0.77	78.94	0.98	0.25	0.63	2.64	0.70	1293957314	81.78	5.69	0.68	46.87	57.65	4.43	90.59	6.36	86.16	45.76	0.48	55.14	Europe
4	China	2022-01-01	7.28	30016.87	0.05	50.33	0.62	0.18	0.92	7.70	0.92	1432971455	82.39	6.33	0.50	60.38	28.54	7.66	59.33	3.00	71.10	65.67	0.12	51.55	Asia

Data Visualization

Asking thoughtful analytical questions

Questions:

1. Has the USA happiness score trended (upward or downward) between 2005 and 2024?
2. What factors correlate most strongly with happiness_score for USA?
3. Is the correlation between Employment_Rate and Happiness statistically significant?
4. What might be causing the downward trend in Happiness_Score since 2021?
5. Is the correlation of Happiness Score with healthy life expectancy from 2021-2024 for the USA statistically significant?

Happiness Score vs Year Linear Regression Fit: 
Slope: 0.001
R-squared: 0.000
P-value: 0.928

Happiness Score vs Employment Rate Linear Regression Fit: 
Slope: 0.010
R-squared: 0.008
P-value: 0.064

Happiness Score vs Healthy Life Expectancy- USA 2021-2024 Linear Regression Fit: 
Slope: 0.046
R-squared: 0.087
P-value: 0.004

Aggregation and Grouping Operations

Perform an aggregation on a groupby

I grouped by Country and Year, then calculated the mean for all numeric columns

	Country	Year	Happiness_Score	GDP_per_Capita	Social_Support	Healthy_Life_Expectancy	Freedom	Generosity	Corruption_Perception	Unemployment_Rate	Education_Index	Population	Urbanization_Rate	Life_Satisfaction	Public_Trust	Mental_Health_Index	Income_Inequality	Public_Health_Expenditure	Climate_Index	Work_Life_Balance	Internet_Access	Crime_Rate	Political_Stability	Employment_Rate	Year_num
0	Australia	2005-01-01	5.430667	28886.978000	0.528667	68.128667	0.372000	0.181333	0.517333	11.041333	0.778000	7.037067e+08	59.478667	5.685333	0.512667	74.255333	41.165333	5.525333	64.565333	6.286667	71.750667	44.341333	0.552667	72.108667	2005.0
1	Australia	2006-01-01	6.030556	28936.431111	0.677222	63.465000	0.495000	0.100556	0.449444	8.176111	0.736667	7.845685e+08	61.441111	6.388333	0.481111	75.430000	36.857222	5.838333	69.659444	5.902222	67.400556	47.636111	0.516111	73.490556	2006.0
2	Australia	2007-01-01	5.600000	36317.625455	0.505000	65.911818	0.529545	0.167273	0.487273	9.251818	0.780455	6.854900e+08	64.544545	6.788636	0.530000	71.362727	38.910000	6.015000	66.739091	5.937273	68.869545	46.938636	0.564545	73.953636	2007.0
3	Australia	2008-01-01	5.868889	27944.319444	0.467778	69.021111	0.616667	0.170000	0.480556	13.337222	0.696667	8.398988e+08	64.130556	6.635556	0.530000	60.281667	33.376667	6.148333	59.651111	5.455000	73.648889	47.990556	0.658333	69.658333	2008.0
4	Australia	2009-01-01	5.182857	29764.803810	0.524762	69.346667	0.574762	0.088571	0.549048	11.704762	0.756667	6.325921e+08	60.689524	6.563333	0.436667	66.839048	47.194286	6.183810	55.751905	6.234286	69.601905	37.245238	0.486190	73.119048	2009.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
195	USA	2020-01-01	5.271667	28603.595417	0.494167	68.694167	0.533333	0.162083	0.441667	13.001667	0.792500	7.466417e+08	59.628333	6.985000	0.479167	68.115833	37.422083	5.592083	55.560000	6.275417	71.215417	44.359583	0.460833	76.734167	2020.0
196	USA	2021-01-01	6.193478	27768.738261	0.629565	67.822609	0.413913	0.157391	0.520000	12.670870	0.706522	8.094940e+08	55.241739	6.189565	0.471304	76.129565	43.584783	5.960435	67.486087	6.153478	65.183478	52.734348	0.489565	77.133478	2021.0
197	USA	2022-01-01	5.830370	32318.473704	0.459630	69.417778	0.566296	0.140000	0.521481	9.208889	0.743704	7.504774e+08	62.747778	6.675556	0.428148	78.861852	42.779630	5.602963	64.268889	6.167037	61.077407	38.973333	0.495556	71.130741	2022.0
198	USA	2023-01-01	5.465714	34045.116667	0.534286	61.773333	0.560476	0.168571	0.503810	11.015238	0.729524	7.384771e+08	56.519048	6.005714	0.464762	66.837619	41.858095	6.668095	60.468095	6.246190	65.696667	42.794762	0.423333	72.164286	2023.0
199	USA	2024-01-01	5.072857	34432.532857	0.480952	67.951429	0.449524	0.091429	0.662857	10.604762	0.741905	8.471548e+08	63.304286	6.712857	0.440476	69.573810	36.480952	7.103333	60.900000	5.776190	71.822857	42.460952	0.519524	73.016667	2024.0

200 rows × 25 columns

Further visualization of aggregated data

Conclusions

There are multiple data points per country per year in this data set, with no overlap of any columns. It is possible that this data is an amalgamation of other data sets, or that there is a missing column that would explain why there are multiple points within a year (such as different groups being surveyed, different regions within a country, or different time points within a year). There are no clear overall correlations between any of the columns and happiness score. If the data is analyzed for the USA in just the years 2021-2024, there is a slight correlation between healthy life expectancy and happiness score, with both going down together over that time period. From this data, overall happiness over the period 2005-2024 is highest for Australia, lowest for the UK, and the USA ranks 3rd in this group for overall happiness score.